from langchain.llms.base import BaseLLM
from typing import Optional, List, Dict, Any
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

class ModelScopeSpeechLLM(BaseLLM):
    def __init__(self, task: str, model_id: str):
        # 加载语音分离模型
        model_id = 'damo/speech_mossformer2_separation_temporal_8k'
        task = Tasks.speech_separation

        # 初始化 ModelScope 语音模型
        self.model = pipeline(task=task, model=model_id)

    def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
        # 处理语音输入，假设 prompt 是音频文件路径
        response = self.model(prompt)
        # 可以根据具体需求对 response 做进一步处理
        return response

    @property
    def identifying_params(self) -> Dict[str, Any]:
        return {"model_id": self.model.model_dir}


if __name__ == '__main__':
    # 创建 ModelScope 语音 LLM 实例
    speech_llm = ModelScopeSpeechLLM(task=Tasks.speech_separation,
                                     model_id='damo/speech_mossformer2_separation_temporal_8k')
    # 测试自定义 LLM
    input_audio_path = 'path/to/your/audio.wav'  # 替换为实际的音频文件路径
    output = speech_llm(input_audio_path)
    print(output)
